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PeoplePilot

Senior Forward Deployed Engineer – AI & Data Engineering

Toronto, ON, Canada

💰 **Pay Rate:** CAD $140–$180 · Full-time

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Job details

Pay: 💰 **Pay Rate:** CAD $140–$180
Job type: Full-time
Apply by: Nov 29, 2026
Posting language: English
Posted: Oct 30, 2026

Original job description

# Senior Forward Deployed Engineer – AI & Data Engineering

📍 **Location:** Toronto, Ontario, Canada

🏢 **Work Model:** Hybrid

💰 **Pay Rate:** CAD $140–$180

📅 **Experience:** 10+ years

## About the Role

We are looking for a **Senior Forward Deployed Engineer** who combines production-grade software engineering, data engineering, AI application development, and client-facing consulting experience.

In this role, you will work closely with business users, client subject matter experts (SMEs), product owners, and engineering teams to understand complex business workflows, validate solutions using real client data, overcome technical challenges, and drive solutions from prototype to production launch.

You will own delivery through production deployment, stabilization, adoption, and handover.

## Key Responsibilities

Work with client SMEs, product owners, business users, and engineering teams to understand and map workflows, users, data, constraints, and decision points.

Define the first useful release, measurable success criteria, and a practical roadmap from prototype to production.

Profile and analyze client data using **Python, pandas, PySpark, and SQL** to assess data quality, coverage, volume, distributions, edge cases, and feasibility.

Build and validate working solutions using real client data and APIs, then enhance them for production readiness.

Design, develop, and deploy enterprise AI and data applications using **Python, PySpark, SQL, REST APIs, FastAPI, and cloud data platforms**.

Build Claude-powered applications, copilots, and AI workflows using prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.

Design batch, real-time, and streaming data pipelines across enterprise systems.

Implement **MLOps/LLMOps** capabilities, including lifecycle management, CI/CD, MLflow, monitoring, deployment automation, rollback, and production support.

Create evaluation datasets with client SMEs, measure model and LLM quality, perform structured error analysis, and prioritize improvements.

Resolve data access, API, identity, network, security, infrastructure, and deployment blockers.

Own post-go-live monitoring, incident diagnosis, stabilization, performance, reliability, latency, and cost optimization.

Measure adoption and business impact using agreed KPIs.

Complete structured handovers with runbooks, support ownership, and knowledge transfer.

## Required Skills & Qualifications

**10+ years of experience** in data engineering, AI application development, cloud data platforms, and production software engineering.

Strong hands-on expertise in:

Python and PySpark

SQL and REST APIs

FastAPI

Git and Docker

Kubernetes

Airflow

MLflow

CI/CD

Distributed processing

Experience with **Claude, prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns**.

Strong understanding of ETL/ELT, batch and streaming pipelines, data quality, metadata, lineage, observability, and DataOps.

Experience operationalizing **MLOps/LLMOps**, including model lifecycle management, automated deployment, monitoring, rollback, and production support.

Knowledge of enterprise cloud services across compute, storage, integration, orchestration, networking, monitoring, security, and cost optimization.

Understanding of IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.

Ability to translate business requirements into scalable technical designs, define non-functional requirements, estimate effort, and drive implementation.

Strong client-facing communication and consulting skills.

## Preferred Qualifications

**Banking and Financial Services (BFSI) domain experience**.

Experience with client discovery, rapid prototyping, solution shaping, workshops, and technical presentations.

Bachelor's degree in a relevant discipline.

Certifications in cloud, Kubernetes, AI engineering, MLOps, data engineering, architecture, or security are preferred.

## Key Skills & Search Keywords

## Ideal Candidate Profile

We are looking for a senior technical professional who can combine hands-on software and data engineering with AI application development and client-facing solution delivery. Candidates should be comfortable working with real enterprise data, rapidly building prototypes, deploying production-ready AI solutions, and owning post-launch outcomes.

## How to Apply

Please share your updated resume highlighting your experience in **Python, PySpark, SQL, AI application development, Claude, RAG, vector databases, MLOps/LLMOps, cloud platforms, and client-facing delivery**.

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More about this job

Responsibilities

Partner with client stakeholders and engineering teams to understand workflows, analyze real-world data, and shape AI and data solutions from prototype through production deployment. Own post-launch stabilization, monitoring, adoption, optimization, and handover while measuring technical performance and business impact.

Requirements

Requires 10+ years of experience in data engineering, AI application development, cloud data platforms, and production software engineering, with hands-on expertise in Python, PySpark, SQL, APIs, and production tooling. Candidates should also have experience with Claude-based AI applications, RAG, vector databases, MLOps/LLMOps, enterprise cloud and security practices, and client-facing delivery.

Skills

Python
PySpark
SQL
FastAPI
REST APIs
Claude
Prompt Engineering
RAG
Vector Databases
Semantic Search
Agentic AI
MLOps
LLMOps
Kubernetes
Docker
Airflow

Visa sponsorship

Not detected in the job text

Keywords

Forward Deployed Engineering
AI Engineering
Data Engineering
Python
PySpark
SQL
FastAPI
REST APIs
Claude
Prompt Engineering
RAG
Vector Databases
Semantic Search
Agentic AI
MLOps
LLMOps
MLflow
Kubernetes
Docker
Airflow
CI/CD
ETL/ELT
Streaming Pipelines
Cloud Data Platforms
DataOps
Distributed Processing
Data Quality
BFSI
Banking and Financial Services
Client-Facing Consulting

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